# Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management

arXiv: https://arxiv.org/abs/2402.00515 · HTML: https://mlsanity.com/paper/2402.00515/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States](https://mlsanity.com/paper/2002.05780/) (2020)
- [Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning](https://mlsanity.com/paper/2003.03051/) (2020)
- [FinBrain: When Finance Meets AI 2.0](https://mlsanity.com/paper/1808.08497/) (2018)
- [Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects](https://mlsanity.com/paper/2008.07871/) (2020)

## Built on by (most cited first, 5 of 5)

- [When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents](https://mlsanity.com/paper/2510.11695/) (2025)
- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization](https://mlsanity.com/paper/2509.12753/) (2025)
- [Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation](https://mlsanity.com/paper/2404.08935/) (2024)
- [Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets](https://mlsanity.com/paper/2510.19950/) (2025)

## Abstract (arXiv)

Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulated investment portfolio while neglecting its potential risks under the turmoil of various market conditions in the global or regional sectors. Accordingly, a multi-agent and self-adaptive framework namely the MASA is proposed in which a sophisticated multi-agent reinforcement learning (RL) approach is adopted through two cooperating and reactive agents to carefully and dynamically balance the trade-off between the overall portfolio returns and their potential risks. Besides, a very flexible and proactive agent as the market observer is integrated into the MASA framework to provide some additional information on the estimated market trends as valuable feedbacks for multi-agent RL approach to quickly adapt to the ever-changing market conditions. The obtained empirical results clearly reveal the potential strengths of our proposed MASA framework based on the multi-agent RL approach against many well-known RL-based approaches on the challenging data sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the past 10 years. More importantly, our proposed MASA framework shed lights on many possible directions for future investigation.